EDGE AI BASED PLANT DISEASE DETECTION SYSTEM

Abstract

Agriculture is a crucial industry to humankind's continued existence. Simultaneously, digitalization's pervasive influence made it simpler to accomplish previously challenging jobs in a wide range of disciplines. The agriculture industry, for both the farmer and the consumer, would greatly benefit from technological and digital adaptation. Through the use of technology and consistent monitoring, illnesses can be detected early on and removed, resulting in a higher yield. The economic, social, and political lives of farmers and the entire agricultural industry are profoundly impacted by the health and productivity of their crops. Therefore, in order to detect the illnesses at the proper moment, it is essential to conduct careful monitoring at different phases of crop growth. However, humans may require more than their natural attire, and there may be situations when doing so would be deceiving. Accurate identification requires a system that can automatically recognise and categorise the numerous illnesses that can affect a given crop. The current proposed framework was inspired by this train of thought. The suggested framework is primarily concerned with the transfer learning phenomena based on VGG16, and the "Plant Village" dataset, which contains both damaged and healthy potato and tomato leaves, is being explored for implementation. Keywords: Transfer learning, VGG16, Plant Village.

Plant phenotyping relevance

植物葉の病害状態を画像から分類する計算機手法が研究の中心であり、VGG16転移学習による提案フレームワークを扱っているため。

Code and data availability

The paper describes a VGG16 transfer-learning system on the Plant Village dataset but provides no authors' public dataset, code, model, or supplement URL; Plant Village is cited prior work, and no allowed URLs exist to match.

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